Many applications of natural language processing technologies involve analyzing texts that concern the psychological states and processes of people, including their beliefs, goals, predictions, explanations, and plans. In this paper, we describe our efforts to create a robust, large-scale lexical-semantic resource for the recognition and classification of expressions of commonsense psychology in English Text. We achieve high levels of precision and recall by hand-authoring sets of local grammars for commonsense psychology concepts, and show that this approach can

An analysis of human planning strategies reveals that much of the knowledge that underlies intelligent planning involves commonsense psychology, the way that people think that they think. In this paper we describe our continuing effort to formalize a large-scale theory of commonsense psychology as 30 interrelated content theories in first-order logic. This paper discusses key aspects of the 16 content theories that we have completed, focusing on those that provide an account of how knowledge and intention lead to action, namely, memory, knowledge management, envisionment, goals, planning, and execution. Some of these areas present challenges to many of the simplifying assumptions that have traditionally been made in formal knowledge representation research; others are areas of commonsense knowledge where few formal treatments have previously been attempted. 1

Metacognitive reasoning in computational systems will be enabled by the development of formal theories that have broad coverage over mental states and processes as well as inferential competency. In this paper we evaluate the inferential competency of an existing formal theory of commonsense human memory by attempting to use it to validate the appropriateness of a commonsense memory strategy. We formulate a particular memory strategy (to create an associated obstacle) as a theorem in first-order predicate calculus. We then attempt to validate this strategy by showing that it is entailed by the axioms of the theory we evaluated. These axioms were encoded into the syntax of an automated reasoning system, which was used to automatically generate inferences and search for formal

Children develop certain abilities related to Theory of Mind reasoning, particularly concerning the False-belief Task, between the ages of 3 and 5. This paper investigates whether there is a corresponding change in the frequency of linguistic expressions related to knowledge and belief produced by children around these ages. Automated corpus analysis techniques are used to tag each expression related to knowledge and belief in a large corpus of transcripts of speech from normally developing English-learning children.

Representations of an AI agent’s mental states and processes are necessary to enable metareasoning, i.e. thinking about thinking. However, the formulation of suitable representations remains an outstanding AI research challenge, with no clear consensus on how to proceed. This paper outlines an approach involving the formulation of anthropomorphic self-models, where the representations that are used for metareasoning are based on formalizations of commonsense psychology. We describe two research activities that support this approach, the formalization of broad-coverage commonsense psychology theories and use of representations in the monitoring and control of objectlevel reasoning. We focus specifically on metareasoning about memory, but argue that anthropomorphic self-models

In the context of developing formal theories of commonsense psychology, or how peole think they think, we have developed a formal theory of goals. In it we explicate and axiomatize, among others, the goal-related notions of trying, success, failure, functionality, intactness, and importance.

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Both animals and humans use meta-rules in their daily life, in order to adapt their behavioral strategies on changing environmental situations. Typically, the term meta-rule encompasses those rules that are applied to rules themselves. In cognitive science, conventional approaches for designing meta-rules follow human hard-wired architectures. In contrast to previous approaches, the current work employs evolutionary processes to explore neuronal mechanisms accounting for meta-level rule switching. In particular, we performed a series of experiments with a simulated robot that has to learn to switch between different behavioral rules in order to accomplish given tasks. Continuous time recurrent neural networks (CTRNN) controllers with either a fully connected or a bottleneck architecture were examined. The obtained results showed that different rules are represented by separate selforganized attractors, while rule switching is enabled by the transitions among attractors. Furthermore, the results showed that neural network division into a lower sensory-motorACQUIRING RULES FOR RULES 2 level and a higher cognitive level enhances the performance of the robot in the given tasks. Additionally, meta-cognitive rule processing is significantly supported by the embodiment of the controller and the lower level sensory-motor properties of environmental interaction. Keywords: